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Dynamic adjustment algorithm of equipment maintenance cycle based on substation state operation and maintenance

Author

Listed:
  • Yuping Yan
  • Fangfang Zhou
  • Zhongyue Cai
  • Hongzhi Lu

Abstract

To improve the timeliness and effectiveness of equipment maintenance and ensure the stable operation of power systems, this paper proposes a dynamic adjustment algorithm for equipment maintenance cycles based on substation condition-based operation and maintenance. The algorithm builds the operation and maintenance management framework of smart substation, adopts the distributed multi-sensor mode to sense the operation and maintenance status data of substation equipment, Based on the operation and maintenance status data of substation equipment, analyze the planned maintenance cost and failure cost of substation equipment, determine the dynamic adjustment objective function of the equipment maintenance cycle based on these two costs, and set the constraint condition of the dynamic adjustment target function of the equipment maintenance cycle from the perspective of the total maintenance plan, the coupling constraint between the maintenance start time and the daily state variables of the maintenance plan, after solving the dynamic adjustment objective function of the equipment maintenance cycle, to obtain the optimal dynamic adjustment parameters of the equipment maintenance cycle, ensure that the planned maintenance cost and failure cost of the adjustment are the lowest. Experimental results show that the proposed algorithm effectively perceives substation equipment condition data, dynamically adjusts maintenance cycles, shortens maintenance durations, and improves the reliability coefficient of substation equipment. The application effect is relatively significant.

Suggested Citation

  • Yuping Yan & Fangfang Zhou & Zhongyue Cai & Hongzhi Lu, 2026. "Dynamic adjustment algorithm of equipment maintenance cycle based on substation state operation and maintenance," PLOS ONE, Public Library of Science, vol. 21(5), pages 1-23, May.
  • Handle: RePEc:plo:pone00:0349132
    DOI: 10.1371/journal.pone.0349132
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